Data Engineering Manager
Skills
Continuous ImprovementData ArchitectureData ServicesEvolutionModelingOptimizationService-Level Agreements
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Proven experience leading engineering teams delivering large-scale production data platforms
- Strong foundation in distributed systems engineering and cloud-native architecture
- Deep understanding of ETL and ELT frameworks and orchestration platforms
- Strong expertise in data lakes warehouses and lakehouse architectures
- Solid understanding of Kafka-based architectures and event-driven data patterns
- Proven experience hiring onboarding and retaining high-performing engineers
- Commitment to building an inclusive collaborative and accountable team culture
- Strong sprint planning capacity modeling and roadmap sequencing skills
- Ability to manage competing priorities across reliability feature delivery and technical debt reduction
- Experience establishing engineering KPIs such as reliability latency throughput and deployment frequency
- Great communication skills
- Ability to collaborate with different teams
- Self-motivated and focused on quality
- Commitment to engineering excellence and operational discipline
Nice to have
- Inclusive
- Collaborative
- Accountable
- Self-motivated
- Quality-focused
Day to day
- Lead and scale data engineering teams across batch and streaming platforms, driving technical execution from ingestion through serving layers.
- Architect scalable data platforms with strong modeling standards, efficient query performance, and robust orchestration and storage strategies.
- Collaborate with product and software teams to align roadmap priorities, communicate tradeoffs, and deliver reliable mission-critical data services.